John Hawks is now a tenured professor at the University of Wisconsin, he’s announced in the inaugural post of a four-part series on academic blogging. The entire thing is well worth a read for anyone who hopes to end up in a similar situation.

One social science finding which I’ve wondered about over the past few years is the result that women care much more about the race of a potential mate than men do. The fact that individuals tend to want to mate assortatively with those who share their characteristics is no surprise. Rather, what does surprise are a series of papers that show a very strong asymmetry in strength of preference between males and females. To be crass about it, an attractive warm body will do for a man, but women strongly prefer a body with the packaging of their own race!
First, let’s keep this in perspective, here are the correlations from the GSS for married individuals for several variables of note (I’ve filtered for whites here):
Ethnicity – 0.40
Highest Degree – 0.55
Socioeconomic index – 0.32
I think it’s interesting to note that the variable which reveals meritocratic achievement has the highest correlation. Ethnicity is something you’re born into, and socioeconomic index is a metric which derives from the milieu in which you were raised.
This post is going to review some findings in a paper which attempts to both describe the differences in race preference for dating by race and across genders, and, why those differences might emerge the way that they do. The paper is Racial Preferences in Dating, Review of Economic Studies (click the link to download and read the whole thing yourself!). Here’s the abstract:

We examine racial preferences in dating. We employ a Speed Dating experiment that allows us to directly observe individual decisions and thus infer whose preferences lead to racial segregation in romantic relationships. Females exhibit stronger racial preferences than males. The richness of our data further allows us to identify many determinants of same-race preferences. Subjects’ backgrounds, including the racial composition of the ZIP code where a subject grew up and the prevailing racial attitudes in a subject’s state or country of origin, strongly influence same-race preferences. Older subjects and more physically attractive subjects exhibit weaker same-race preferences.

A few points need to be made clear: males do not exhibit statistically significant racial preferences by and large. That’s somewhat shocking to me. I’m not surprised that older subjects have weaker biases, I suspect frankly they’re more realistic and don’t want to narrow their options anymore than they have to. Finally, I’m totally confused as to why hotties would be less race conscious; you would figure if hybrid vigor is real that the marginal returns would be greatest for the fuglies (specifically, assuming that fugitude correlates with individual mutational load and hybridization would be better at masking that load). But the most relevant demographic point is that these are Columbia University graduate students. In other words, a cognitively & socially elite sample.

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In my recent note on R. A. Fisher and epistasis, I mentioned that Fisher’s theory of the evolution of dominance relied on the epistatic effect of ‘modifier’ genes. On looking again at the chapter in The Genetical Theory of Natural Selection dealing with the evolution of dominance, I see that there is a more general statement of the principle that the effect of a gene depends in part on the genetic background against which it occurs:

The fashion of speaking of a given factor, or gene substitution, as causing a given somatic change, which was prevalent among the earlier geneticists, has largely given way to a realization that the change, although genetically determined, may be influenced or governed either by the environment in which the substitution is examined, or by the other elements in the genetic composition. Cases were fairly early noticed in which a factor, B, produced an effect when a second factor, A, was represented by its recessive gene, but not when the dominant gene was present. Factor A was then said to be epistatic to factor B, or more recently B would be said to be a specific modifier of A. …. These are evidently only particular examples of the more general fact that the visible effect of a gene substitution depends both on the gene substitution itself and on the genetic complex, or organism, in which this gene substitution is made.
– The Genetical Theory of Natural Selection, page 54, variorum edition, 1999, from the first edition text of 1930. There is a slight change of wording in the second (1958) edition.

Over the past few days I’ve blogged a bit about the story about an HIV susceptibility allele; Evolution, a reason for the African HIV epidemic?, Overplaying “AIDS genes” and HIV susceptibility, a “black” thing, not a Duffy thing?. But there’s an important post Genetic Future, Duffy-HIV association: an odd choice of ancestry markers:

In the Duffy study the authors attempt to perform this type of correction using a set of just 11 markers they describe as “differentially distributed between European and African populations”. p-ter notes that several of these markers are not particularly ancestry-informative, and indeed on closer inspection it’s clear why this is: these genes weren’t originally selected on the basis of ancestry informativeness, but rather because they are associated with HIV biology. Every single one of the 11 markers has some association with HIV: three of them have previously been associated with HIV infection, progression, or response to treatment (CCR5 delta32, APOBEC3G H186R, GNB3 C825T); most of the remaining markers are in genes that are known binding targets or modulators of HIV (CCR5, CXCR4, PD1, TRIM5, IL-2, IL-4).
…
If that’s true – and it’s difficult to see any other rationale for using these HIV markers rather than a set of validated AIMs – this is poor form for at least two reasons. Firstly, it’s unlikely that using such a weak set of ancestry-informative markers provides an effective correction for a marker with as strong a correlation with ancestry as Duffy (as p-ter notes, all of the supposed ancestry markers are far weaker predictors of ancestry than the Duffy variant). Secondly, testing several different variants for an association with HIV and then only reporting the one that achieved significance creates the perfect conditions for a false positive due to multiple comparisons – it’s entirely possible that the Duffy association would not have survived correction for multiple testing. It’s difficult to assess this fully because the manuscript doesn’t seem to report a single P value (!), although I note that the lower edge of the 95% confidence interval of the odds ratio in Figure 2C is perilously close to 1 following their ancestry “correction”.

Read the whole thing…but something is starting to smell fishy. Hey Dave Appell, blogs rock and peer review sucks! (sometimes)

Just a quick follow up to my post about genetic screening of embryos and subsequent implantation. The spontaneous abortion rate for humans is very high. Probably on the order of 50% of fertilized ova implant and complete to term. I’ve seen numbers all over the place. In any case, I assume many of these are chromosomal abnormalities. But I’ve also posted to data which strongly suggests that immunological incompatibilities between mother & fetus also play a role in spontaneous abortions and may result in natural selection which we’re not too well aware of. In The Cooperative Gene the evolutionary biologist Mark Ridley suggests that because of spontaneous abortion we should be less than worried about reduced efficacy of natural selection in purging deleterious alleles from the gene pool; as the genetically unfit reach viability and reproduce their alleles will be culled at the stage of of the embryo and fetus.

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DARC and HIV: a false positive due to population structure?:

The authors are aware of this potential confounder, and develop a measure of admixture based on 11 SNPs to include as a covariate in their regression. However, this measure is kind of weak, which I imagine in the sticking point for the skeptics in the Times article. If you have access to the supplemental information, take a look at it–several of these 11 SNPs are in the same gene, which means they’re not independent, and several don’t even have big frequency differences between African and European samples (if you’re trying to judge via SNPs whether someone is more African or European, those SNPs better have a big frequency difference between Africa and Europe). This is probably not a precise measure of ancestry. In fact, the Duffy null allele they claim as associated is a better predictor of ancestry than any of these SNPs.
So it’s quite possible that the authors have simply shown a correlation between level of African ancestry and susceptibility to HIV (which could be due to any number of sociological, demographic, or genetic factors), rather than an association between Duffy null and susceptibility to HIV. Here’s a relatively simple test of this possibility: genotype rs1426654 (the nonsynonymous SNP in SLC24A5) in their sample and perform exactly the same test as performed with Duffy. The motivation for this is that this SNP shares the property of Duffy null of being highly informative about ancestry, while being in a gene that presumably plays no role in HIV infection. If you get an association there, it seriously calls the Duffy result into question; if not, you feel a bit more comfortable.

This is why mapping human variation is so important. Note that the genetic variation itself might not even be responsible for greater susceptibility to HIV in a causal sense; if circumcision does have a role in reducing the spread of HIV then cultural practices which correlate with genetic variation can also produce spurious correlations. As an illustrative example in South Africa there is a correlation between HIV infection rates and decreased Khoisan ancestry. I say this because the Zulus have higher infection rates than the Xhosa, and the latter have a great deal more Khoisan ancestry than the former. The differences have been attributed to the fact that the Xhosa practice circumcision and the Zulu do not, so the differences in genes is just coincidental.

The recent report that the Duffy null allele is associated with increased risk of HIV infection recieved a lot of press (see Razib’s comments on it here), mostly positive. In Nick Wade’s New York Times article on the paper, however, some smart people publicly express some doubts. It’s a tribute to Wade that he actually tries to summarize those doubts in the limited space allotted to him:

Dr. Goldstein said that in parts of the United States, African-Americans have a higher infection rate than European-Americans, and that patients with a higher proportion of African genes may be more vulnerable to H.I.V. for reasons unconnected to the SNP. Nonetheless, the SNP would show up in a greater proportion of infected people simply because of their African heritage. If so, the gene’s apparent association with H.I.V. infection could be just coincidental, not causal.

In somewhat more technical terms, the issue referred to here is the potential for false positives in an association study due to population structure[1]. The issues involved in accounting for structure in an admixture mapping study are somewhat more subtle than in a classic case-control study, but are generally similar. In particular, it’s important to take individual levels of admixture into account[2]; this is generally done by including an estimate of individual admixture as a covariate in any regression model.

The authors are aware of this potential confounder, and develop a measure of admixture based on 11 SNPs to include as a covariate in their regression. However, this measure is kind of weak, which I imagine in the sticking point for the skeptics in the Times article. If you have access to the supplemental information, take a look at it–several of these 11 SNPs are in the same gene, which means they’re not independent, and several don’t even have big frequency differences between African and European samples (if you’re trying to judge via SNPs whether someone is more African or European, those SNPs better have a big frequency difference between Africa and Europe). This is probably not a precise measure of ancestry. In fact, the Duffy null allele they claim as associated is a better predictor of ancestry than any of these SNPs.

So it’s quite possible that the authors have simply shown a correlation between level of African ancestry and susceptibility to HIV (which could be due to any number of sociological, demographic, or genetic factors), rather than an association between Duffy null and susceptibility to HIV. Here’s a relatively simple test of this possibility: genotype rs1426654 (the nonsynonymous SNP in SLC24A5) in their sample and perform exactly the same test as performed with Duffy. The motivation for this is that this SNP shares the property of Duffy null of being highly informative about ancestry, while being in a gene that presumably plays no role in HIV infection. If you get an association there, it seriously calls the Duffy result into question; if not, you feel a bit more comfortable.

[1] For the classic extreme example of how population structure leads to false positive associations, consider a case-control association study on, say, diabetes, where the cases are all from Nigeria and the controls from France. Clearly, the cases are all going to have a high frequency of the Duffy null allele, and the controls are all going to have a low frequency (as Duffy null is essentially fixed in Africa and absent elsewhere), and one might naively conclude that Duffy null causes diabetes. But of course, the Duffy blood group has absolutely nothing to do diabetes (I don’t think!), and the researchers have simply been confused by not matching their cases and controls. Obviously, this example is extreme, but more subtle population structure can also confound an association study (and methods for correcting for it are an active area of research; see here, for example)

[2] It’s well-known that African-Americans are an admixed population, with about 15-20% European ancestry on average. But there’s great variability in this–a single sample of self-defined “African-Americans” can contain individuals with essentially no European ancestry and individuals who look genetically to be completely European. And on a larger scale, within the United States there’s heterogeneity in admixture proportions as well (see Parra et al.). How could this create false positives? Essentially, if risk for a disease is correlated with ancestry for any reason, there’s the potential for getting false positives. In this particular example, if HIV rates are higher in metropolitan areas where there’s been more admixture, or if there are other genetic factors that make Europeans more resistant to HIV, etc., any “African allele” (like Duffy null) will show up as associated with HIV despite playing absolutely no role in the disease.

Genetic Future points me to a Nature News story, Making babies: the next 30 years. He highlights this section:

There’s speculation that people will have designer babies, but I don’t think the data are there to support that. The spectre of people wanting the perfect child is based on a false premise. No single gene predicts blondness or thinness or height or whatever the ‘perfect baby’ looks like. You might find genetic contributors but there are so many environmental factors too.

The details are important here. Height is a tough cookie; it seems like there are going to at least hundreds of loci which control most of the normal human variation (if not thousands). But blondness is a bad example; pigmentation is only controlled by a few loci. Getting a fix on OCA2, SLC45A2, SLC24A5 and KITLG will get you what you want. That doesn’t mean the parents don’t have to have the variation necessary, they do, but they can probably guarantee a little blonde beast if they have the potentiality. And it seems that this really isn’t a function of the genetic future, the number of embryos parents would need to guarantee a little Aryan baby if they both have Aryan blood isn’t that high (probably 10-100 embryos depending on the purity of Aryan blood of the parents).
In any case, read the Genetic Future post for the bigger picture.

Gintis and Bowles have done great work cleaning up a lot of the discussion about cooperation, evolution, and economic outcomes. A Google Scholaring of their names turns up 14 items with over 100 citations, most of which would be well worth reading for GNXP regulars.

But that said, in their 2002 Journal of Economic Perspectives piece “The Inheritance of Inequality,” they appear to make a small error. It’s an error that’s all-too-easy for even good folks to make: They apparently squared the h-squared.

Their big insight and their small error are all part of answering a simple question: How much of the correlation of income between parent and child can be explained by the heritability of IQ? You might think it’s straightforward: IQ is highly heritable, so if there’s some channel linking IQ to income, then it’s all over but the shouting.

But numbers matter. And Gintis/Bowles work out the numbers, finding that there’s a weak link in that causal chain: The low correlation (0.27 according to Gintis and Bowles) between IQ and wages. The causal chain goes like this:

1. Parental earnings have a 0.27 correlation with parent’s IQ.
2. Heritability of IQ between parent and child is a bit more than 1/2 of h-squared (why a bit more? assortive mating). They take an h-squared of 0.5 for IQ.
3. Child’s earnings have a 0.27 correlation with child’s IQ.

So the net result is 0.27*0.3*0.27 = 0.022 (page 10). A very small number, especially since the raw parent-child income correlation in U.S. data is about 0.4. So yes, knowing a parent’s income helps you predict their adult (especially male) child’s income. But only 5% (or 0.022/0.4) of the total correlation can be explained by IQ’s impact on wages. Small potatoes.

(Oh, but where’s the small error? It’s where Gintis and Bowles report that the net result is 0.01 instead of 0.022–a difference that I can most easily attribute to a mistaken squaring of the h-squared.)

If I really wanted to get that net result up from a measly 5%–if I knew in my heart that IQ really was a driving force in intergenerational income inequality–then how would I do it? Well, I might use a higher heritability of IQ, I might assume more assortive mating, or I might assume a bigger correlation between wages and IQ.

Hard to do much to budge that IQ/wage link: Zax and Rees’s paper only has a 0.3 correlation between teenage IQ and middle-aged wages, and when Cawley, Heckman et al. regress NLSY wages on the first 10 principal components of the AFQT, they get a similar result.

So you think maybe a higher heritability of IQ will save you? Well, let’s just go all the way to perfect heritability of IQ and perfect assortive mating on IQ. In other words, let’s see if “IQ clones” will be have enough similarity in wages to match the 0.4 intergenerational correlation of income.

Will the IQ clones have similar incomes? Not so much. (0.3^2)*1 still equals something small: 0.09. Less than 1/4 of the intergeneration correlation in income. Medium-sized potatoes, but we had to make a ton of ridiculous assumptions to get there.

It’s that doggone low correlation between IQ and wages, a correlation that has to be squared because we’re comparing parent to child. So a high heritability of IQ doesn’t imply a high heritability of IQ-caused-income. Another reminder that lots of things impact your wages: Not just how smart you are.

Gintis and Bowles work through some finger exercises to argue for big environmental effects, and that’s all well and good. But to my mind, the interesting fact is that income is still highly heritable!

G/B report that MZT (identical twin) earnings correlation is 0.56, and DZT (fraternal twin) earnings correlation is 0.36, so using the crudest of approximations, the heritability of earnings is still (0.56-0.36)*2=0.4. So income apparently has a modestly high heritability, but most of it can’t be explained by the IQ-wage channel. Looks like the genetic heritability of income is being driven mostly by non-IQ channels.

David takes a slight detour in this Sewall Wright, series, R. A. Fisher and Epistasis:

My next note on Sewall Wright will cover the exciting subject of the adaptive landscape. As every schoolboy knows, Wright considered epistatic gene interactions very important in determining the ‘peaks’ of the landscape. A sharp contrast is sometimes drawn between Wright and R. A. Fisher in this respect….

This is a preamble to a very long and dense post. If it interests you in the subject, I’d also recommend Epistasis and the Evolutionary Process. You might also check out this older post of mine on evolutionary epistasis.
Related: Notes on Sewall Wright: Population Size, Notes on Sewall Wright: the Measurement of Kinship, Notes on Sewall Wright: Path Analysis, On Reading Wright and Notes on Sewall Wright: Migration.